用AI自动提取标准文件中的决策规则，生成可解释的结构化报告
Retrieval Augmented Decision-Making: A Requirements-Driven, Multi-Criteria Framework for Structured Decision Support
- 结合多准则决策与大模型语义理解，自动生成加权决策树
- 在多个任务中生成报告的细节、合理性和结构性显著优于现有方法
- 适合需要透明、可追溯决策支持的工业和监管场景
各行业积累了大量结构复杂、内容碎片化的文档，如产业规划、技术规范和法规，给专家决策带来检索与理解挑战。现有基于大模型的检索增强生成方法虽能提供上下文建议，但缺乏量化权重与可追溯推理路径，难以实现多层次、透明的决策支持。为此，本文提出RAD方法，将多准则决策与大模型语义理解能力融合。该方法可自动从行业文档中提取关键决策标准，构建加权层次化决策模型，并在模型引导下生成结构化报告。RAD框架通过显式权重分配与推理链设计，确保决策结果的准确性、完整性和可追溯性。实验表明，在多种决策任务中，RAD生成的报告在细节、合理性与结构上显著优于现有方法，展现出在复杂决策支持场景中的应用价值与潜力。
原文摘要 · Abstract (English)
Various industries have produced a large number of documents such as industrial plans, technical guidelines, and regulations that are structurally complex and content-wise fragmented. This poses significant challenges for experts and decision-makers in terms of retrieval and understanding. Although existing LLM-based Retrieval-Augmented Generation methods can provide context-related suggestions, they lack quantitative weighting and traceable reasoning paths, making it difficult to offer multi-level and transparent decision support. To address this issue, this paper proposes the RAD method, which integrates Multi-Criteria Decision Making with the semantic understanding capabilities of LLMs. The method automatically extracts key criteria from industry documents, builds a weighted hierarchical decision model, and generates structured reports under model guidance. The RAD framework introduces explicit weight assignment and reasoning chains in decision generation to ensure accuracy, completeness, and traceability. Experiments show that in various decision-making tasks, the decision reports generated by RAD significantly outperform existing methods in terms of detail, rationality, and structure, demonstrating its application value and potential in complex decision support scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。